Category Archives: Anthropology

AAPA 2012 Run-Down

I am done with this year’s American Association of Physical Anthropologists annual meeting in Portland. Alas, I am not yet home as I had a scheduling snafu with Alaska Airlines yesterday and there was literally not a single seat on a flight to any airport in the Bay Area. So, I hung out in PDX for the night, where my sister-in-law is finishing up her MD/MPH at OHSU. Staying an extra night allowed me to have dinner at what is probably my favorite pizzaria on the West Coast, Bella Faccia on Alberta Ave in Northheast (Howie’s in Palo Alto is a close second). I also had a lovely breakfast of rissotto cakes and poached eggs at Petite Provance, also on Alberta. All in all, a fantastic couple days’ worth of food.
It was great to get a chance to catch up with old friends and colleagues and meet new ones. This is really what professional meetings are about. I had a chance to spend time with Charles Roseman, Rick Bribiescas, Josh Snodgrass, Nelson Ting, and Frances White. I also had very nice, if too brief, chats with Connie Mulligan, Lorena Madrigal, Larry Sugiyama, Greg Blomquist, Zarin Machanda, Melissa Emery Thompson, Cheryl Knott, and Chris Kuzawa.
I only go to the AAPAs every couple of years. Given the interdisciplinarity of my work and interests, I struggle to find a “home” professional meeting. Sometimes I feel like it’s PAA; sometimes Sunbelt; sometimes AAPA/HBA.  One thing I can say for certain is that it is not AAA, my semi-annual experience in ethnographic surreality. Such a peculiar discipline anthropology is. Part of the reason I don’t go to AAPAs all that often is that I rarely find all that much interesting there. There are a few really fantastic people working in the field but most of the talks I find stupifyingly boring. I’m just not that interested in teeth. I suppose this is true for any professional meeting, so I shouldn’t be too hard on AAPA — I’m also not that interested in contraceptive uptake, social media/online networks, or governmentality, apparently the modal topics in my competing meetings. In fact, I was pleasantly surprised by the diversity and quality of talks I saw at AAPA this year.
In my session alone, I saw really terrific and interesting talks by Steve Leigh and Connie Milligan. Steve spoke on the comparative gut microbiomes of primates and Connie presented early results on the modification of gene expression through methylation of infants born to women who experienced extreme psychosocial and physical trauma in eastern Congo. Really important stuff. It also struck me that you’d probably only see these types of talks at the AAPAs.
There were a lot of young people at this meeting — a greater fraction than I remember from past meetings.  Maybe it was the draw of hipster Portland with its great beer, great food, and general atmosphere of grooviness. Maybe there really are lots and lots of young physical anthropologists being trained these days. I must admit that I had mixed feelings about this thought as I looked out over the vast ocean of twenty-something faces in the hotel bar Saturday night. On the one hand, it’s great that people are being trained to do good work in physical anthropology. On the other hand, I worry about the ability of our discipline, which shows no signs of stopping with the charade that somehow anthropology is really akin to literary criticism, to absorb this many new Ph.D.s from (one of) the scientific wings of modern anthropology.
Two of the talks immediately before me in my session were, in fact, by young scientists and they were great. Andrew Paquette, from Northern Arizona University, gave a talk on the evolutionary history of Southeast Asian Ovalocytosis (SAO), a twenty-seven base pair deletion in the eleventh exon of the SLC4A1 gene that confers strong protection against infection with Plasmodium falciparum, the most dangerous form of malaria. Turns out this mutation, which has its geographic epicenter in Nusa Tenggara in Indonesia, is surprisingly ancient. Lots more to come from this, I’m sure. Margaux Keller, from Temple, gave a fantastic talk on finding some of the missing heritability in Parkinson’s disease. Missing heritability of complex disease phenotypes is a major topic in genetic epidemiology and Margaux and her colleagues applied Genome-Wide Complex Trait Analysis to eight cohorts of case-control studies of PD. Their results substantially increase (i.e., by a factor of 10!) the fraction of total phenotypic variance in PD explained by straight-up genome-wide association studies (GWAS). In addition to the excellent scientific content of her presentation, I was struck by the very nice and original visual aesthetic of her slides.
I spoke on my recent work on the quantiative genetics of life-history traits.  With Statistics grad student Philip Labo, I’ve been doing some pretty serious number-crunching to examine the heritabilities of and (more interestingly) genetic correlations between human life-history characters. Good results that should be seeing some more light soon (including at PAA next month!).

I am done with this year’s American Association of Physical Anthropologists annual meeting in Portland. Alas, I am not yet home as I had a scheduling snafu with Alaska Airlines yesterday and there was literally not a single seat on a flight to any airport in the Bay Area. So, I hung out in PDX for the night, where my sister-in-law is finishing up her MD/MPH at OHSU. Staying an extra night allowed me to have dinner at what is probably my favorite pizzeria on the West Coast, Bella Faccia on Alberta Ave in Northeast (Howie’s in Palo Alto is a close second). I also had a lovely breakfast of risotto cakes and poached eggs at La Petite Provence, also on Alberta. All in all, a fantastic couple days’ worth of food.

It was great to get a chance to catch up with old friends and colleagues and meet new ones. This is really what professional meetings are about. I had a chance to spend time with Charles Roseman, Rick Bribiescas, Josh Snodgrass, my EID buddy Nelson Ting, Kirstin Sterner, and Frances White. I also had very nice, if too brief, chats with Connie Mulligan, Lorena Madrigal, Larry Sugiyama, Greg Blomquist, Zarin Machanda, Melissa Emery Thompson, Cheryl Knott, Andy Marshall, and Chris Kuzawa.

I only go to the AAPAs every couple of years. Given the interdisciplinarity of my work and interests, I struggle to find a “home” professional meeting. Sometimes I feel like it’s PAA; sometimes Sunbelt; sometimes AAPA/HBA.  One thing I can say for certain is that it is not AAA, my semi-annual experience in ethnographic surreality. Such a peculiar discipline anthropology is. Part of the reason I don’t go to AAPAs all that often is that I rarely find all that much interesting there. There are a few really fantastic people working in the field, but most of the talks I find stupifyingly boring. I’m just not that interested in teeth. I suppose this is true for any professional meeting, so I shouldn’t be too hard on AAPA — I’m also not especially interested in contraceptive uptake, social media/online networks, or governmentality, apparently the modal topics in my competing meetings. In fact, I was pleasantly surprised by the diversity and quality of talks I saw at AAPA this year.

In my session alone, I saw really terrific and interesting talks by Steve Leigh and Connie Mulligan. Steve spoke on the comparative gut microbiomes of primates and Connie presented early results on the modification of gene expression through methylation of infants born to women who experienced extreme psychosocial and physical trauma in eastern Congo. Really important stuff. It also struck me that you’d probably only see these types of talks at the AAPAs.

There were a lot of young people at this meeting — a greater fraction than I remember from past meetings.  Maybe it was the draw of hipster Portland with its great beer, great food, and general atmosphere of grooviness. Maybe there really are lots and lots of young physical anthropologists being trained these days. I must admit that I had mixed feelings about this thought as I looked out over the vast river of twenty-something faces pouring into the hotel bar Saturday night. On the one hand, it’s great that people are being trained to do good work in physical anthropology. On the other hand, I worry about the ability of our discipline, which shows no signs of stopping with the charade that somehow anthropology is really akin to literary criticism, to absorb this many new Ph.D.s from (one of) the scientific wings of modern anthropology.

Two of the talks immediately before me in my session were, in fact, by young scientists and they were great. Andrew Paquette, from Northern Arizona University, gave a talk on the evolutionary history of Southeast Asian Ovalocytosis (SAO), a twenty-seven base pair deletion in the eleventh exon of the SLC4A1 gene that confers strong protection against infection with Plasmodium falciparum, the most dangerous form of malaria. Turns out this mutation, which has its geographic epicenter in Nusa Tenggara in Indonesia, is surprisingly ancient. Lots more to come from this, I’m sure. Margaux Keller, from Temple, gave a fantastic talk on finding some of the missing heritability in Parkinson’s disease. Missing heritability of complex disease phenotypes is a major topic in genetic epidemiology and Margaux and her colleagues applied Genome-Wide Complex Trait Analysis to eight cohorts of case-control studies of PD. Their results substantially increase (i.e., by a factor of 10!) the fraction of total phenotypic variance in PD explained compared to straight-up genome-wide association studies (GWAS). In addition to the excellent scientific content of her presentation, I was struck by the very nice and original visual aesthetic of her slides.

I spoke on my recent work on the quantitative genetics of life-history traits.  With Statistics grad student Philip Labo, I’ve been doing some pretty serious number-crunching to examine the heritabilities of and (more interestingly) genetic correlations between human life-history characters. Good results that should be seeing some more light soon (including at PAA next month!).

Wealth and Cheating

I recently read a story in the Los Angeles Times about a team of psychologists at UC Berkeley who showed, in a series of experimental and naturalistic studies, that wealthy individuals are more likely to cheat or violate social norms about fairness. The Story in the Times referred to the paper by Piff et al. in the 27 February edition of PNAS.  Here is the abstract of this paper:

Seven studies using experimental and naturalistic methods reveal that upper-class individuals behave more unethically than lower-class individuals. In studies 1 and 2, upper-class individuals were more likely to break the law while driving, relative to lower-class individuals. In follow-up laboratory studies, upper-class individuals were more likely to exhibit unethical decision-making tendencies (study 3), take valued goods from others (study 4), lie in a negotiation (study 5), cheat to increase their chances of winning a prize (study 6), and endorse unethical behavior at work (study 7) than were lower-class individuals. Mediator and moderator data demonstrated that upper-class individuals’ unethical tendencies are accounted for, in part, by their more favorable attitudes toward greed.

This study was apparently motivated by observations that people in expensive luxury cars are more likely to bolt ahead of their turn at four-way stop intersections in the San Francisco Bay Area, a daily experience for anyone driving in Palo Alto! It’s terrific that these authors actually took the trouble to systematize their casual observations of driving behavior and make an interesting and compelling scientific statement.

On Friday, I made my own observations about class, cheating, and the violation of norms as I flew down to LAX to attend Sunbelt XXXII (the annual conference for the International Network for Social Network Analysis). Of late, I’ve racked up a lot of miles on United and, as a result, occasionally get upgraded to first class or business class seating. My trip Friday was one of those occasions. As I sat in the (relatively) comfy leather seat of the first-class cabin reading Jeremy Boissevain’s rather appropriate (1974) book Friends of Friends: Networks, Manipulators, and Coalitions, I noticed that nearly everyone around me was busily chatting away or otherwise fiddling around with their smart phones. When the cabin door finally closed and the announcement was made requesting that phones be switched off, none of the people in my neighborhood did so. They put their phones down or in their shirt pockets and watched the flight attendants.  When the flight attendants passed through the cabin and were occupied with other business, out came the smart phones again. The one gentleman across the aisle from me looked like a school kid writing a note in class or something. He kept a wary half-eye out for the flight attendants and looked extremely guilty about his actions, but he nonetheless kept doing his, no doubt, extremely important business.  The man on the phone in the row ahead of me was a little more shameless. He seemed completely unconcerned that he might get busted. The woman in the row ahead of me and across the aisle moved her phone so that it was partially hidden by the arm-rest of her seat as she continued to scroll through her very, very important email. Of the six people I could easily see in my neighborhood, fully half of them continued to use their phones right into taxi and take-off.  Based on their attempts at concealment, at least two of them knew what they were doing was wrong. Now, any regular traveler has seen people using their phones on the plane after they are supposed to. However, I had never seen this sort of density of norm violation on a single flight before.

Of course, this is an anecdote but the study by Piff et al. (2012) shows how anecdotes about social behavior can go on to be systematized into interesting scientific studies.

Get Off the Sexual Network

When I was in Uganda last month, I was talking with collaborators, field assistants, villagers, taxi drivers, bartenders – pretty much anyone who would listen – about social networks, I was struck by what a sophisticated understanding of social networks my average interlocutor had.  As part of our project examining the risk of zoonotic disease spillover in rural Uganda, we are gathering data on individual people’s personal networks. We are interested in contact networks, for sure, but we are also examining people’s social capital – the resources to which an individual has access for instrumental action that are embedded in his or her social network. There are generally two classes of definitions of social capital used in the literature. The first, made famous by Robert Putnam‘s book, Bowling Alone, is really a measure of community solidarity. How cohesive are communities and how does this contribute to individuals’ and communities’ welfare?  The definition I typically employ is attributable to Bourdieau and a host of other scholars, especially Nan Lin. This definition emphasizes both the networked nature of social capital and the instrumentality of it.

The reasoning behind doing a social capital inventory in conjunction with our study of zoonotic disease spillover risk is to have a thorough description of the “state” of individuals. Social surveys typically measure income, household wealth, land holdings, etc. One measures such things in a social survey because one is interested in the economic state of the individual or household in which she is embedded. Social capital is a measure of economic – and social – well-being for people where many of the resources that they need to succeed, or even just get by, are not specifically located in the household or with the individual. We suspect that people in rural Uganda will vary in the amount of social capital they have and that this may be a major axess of vulnerability.

So, there I am, talking to anyone who would listen about the best way to gather information on personal social networks and it turns out that everyone I spoke with was amazingly familiar with the whole concept of social networks. The catch is, the networks with which they are familiar are a special type of networks – sexual networks. When I asked how everyone seemed to know so much about sexual networks, they pointed me to a public-service advertising campaign for which the tag line is “get off the sexual network.” Despite the Central African origin of HIV-1, Uganda was an early center for the epidemic. However, as noted by Stoneburner and Lowbeer, in their important 2004 paper, Uganda experienced substantial – and early – decline in HIV-1 incidence because of health communication through social networks. They write:

The response in Uganda appears to be distinctively associated with communication about acquired immunodeficiency syndrome (AIDS) through social networks. Despite substantial condom use and promotion of biomedical approaches, other African countries have shown neither similar behavioral responses nor HIV prevalence declines of the same scale. The Ugandan success is equivalent to a vaccine of 80% effectiveness. (Stoneburner & Lowbeer 2004)

I definitely need to check out the current state of the art to see if other countries in Sub-Saharan Africa have now experienced similar public health gains as a result of network-oriented interventions.

Based on my rather unsystematic sample, I’d say that this campaign has really worked raise people’s understanding of relational interconnectedness.  I was not able to get a picture of the huge billboards on the Kampala-Entebbe Highway (because it was always dark when I drove by them) but the TV ad is available on youtube. On the one hand, this is really great (both for the obvious public health reasons and because people seem to have a good understanding of webs of social relations). On the other hand, it will probably mean we will need to work hard to clarify what types of networks we mean when we gather our network data.

Three Questions About Norms

Well, it certainly has been a while since I’ve written anything here. Life has gotten busy with new projects, new responsibilities, etc. Yesterday, I participated in a workshop on campus sponsored by the Woods Institute for the Environment, the Young Environmental Scholars Conference. I was asked to stand-in for a faculty member who had to cancel at the last minute. I threw together some rather hastily-written notes and figured I’d share them here (especially since I spoke quite a bit of the importance for public communication!).

The theme of the conference was “Environmental Policy, Behavior, and Norms” and we were asked to answer three questions: (1) What does doing normative research mean to you? (2) How do your own norms and values influence your research? (3) What room and role do you see for normative research in your field? So, in order, here are my answers.

What does doing normative research mean to you?

I actually don’t particularly like the term “normative research” because it sounds a little too much like imposing one’s values on other people. I am skeptical of the imposition of norms that have more to do with (often unrecognized) ideology and less about empirical truth – an idea that was later reinforced by a terrific concluding talk by Debra Satz. If I can define “normative” to mean with the intent to improve people’s lives, then OK.  Otherwise, I prefer to do “positive” research.

For me, normative research is about doing good science. As a biosocial scientist with broad interests, I wear a lot of hats. I have always been interested in questions about the natural world, and (deep) human history in particular. However, I find that the types of questions that really hold my interest these days are more and more engaged in the substantial challenges we face in the world with inequality and sustainability. In keeping with my deep pragmatist sympathies, I increasingly identify with Charles Sanders Pierce‘s idea that given the “great ocean of truth” that can potentially be uncovered by science, there is a moral burden to do things that have social value. (As an aside, I think that there is social value in understanding the natural world, so I don’t mean to imply a crude instrumentalism here.) In effect, there is a lot of cool science to be done; one may as well do something of relevance.  I personally have little patience for people who pursue racist or otherwise socially divisive agendas and cloak their work in a veil of  free scientific inquiry.  This said, I worry when advocacy interferes with intellectual fairness or an unwillingness to accept that one’s position is not actually true.

I think that we are fooling ourselves if we believe that our norms somehow don’t have an effect on our research.  Recognizing what these norms that shape your research – whether implicitly or explicitly – helps you manage your bias. Yes, I said manage. I’m not sure we can ever completely eliminate it. I see this as more of a management of a necessary trade-off, drawing an analogy between the practice of science and a classic problem in statistics, between bias and variance. The more biased one is, the less variance there is in the outcome of one’s investigation. The less bias, the greater the likelihood that results will differ from one’s expectations (or wishes). Recognizing how norms shape our research also deals with that murky area of pre-science: where do our ideas for what to study come from?

How do your own norms and values influence your research?

Some of the the norms that shape my own research and teaching include:

transparency: science works best when it is open. This places a premium on sharing data, methods, and communicating results in a manner that maximizes access to information. As a simple example, this norm shapes my belief that we should not train students from poor countries in the use of proprietary software (and other technologies) that they won’t be able to afford when they return to their home countries when there are free or otherwise open-source alternatives.

fairness: this naturally includes a sense of social justice or people playing on an equal playing field, but it also includes fairness to different ideas, alternative hypotheses, the possibility that one is wrong. This type of fairness is essential for one’s credibility as a public intellectual in science (particularly supporting policy), as noted eloquently in this interview with Dick Lewontin.

respect for people’s ultimate rationality: Trying to understand the social, ecological, and economic context of people’s decision-making, even if it violates our own normative – particularly market-based economic – expectations.

flexibility: solving real problems means that we need to be flexible in our approach, willing to go where the solutions lead us, learning new tools and collaborating. Flexibility also means a willingness to give up on a research program that is doing harm.

good-faith communication: I believe that there is no room for obscurantism in the academy of the 21st century. This includes public communication. There are, of course, complexities here with regard to the professional development of young scholars.  One of the key trade-offs for young scholars is the need for professional advancement (which comes from academic production) and activism, policy, and public communication. Within the elite universities, the reality is that neither public communication nor activism count much for tenure. However, as Jon Krosnick noted, tenure is a remarkable privilege and, while it may seem impossibly far away for a student just finishing a Ph.D., it’s not really. Once you prove that you have the requisite disciplinary chops, you have plenty of time to to use tenure for what it is designed for (i.e., protecting intellectual freedom) and engaging in critical public debate and communication.

humility: solving problems (in science and society) means caring more about the answer to a problem than one’s own pet theory. Humility is intimately related to respect for others’ rationality.  It also means recognizing the inherently collaborative nature of contemporary science: giving credit where it is due, seeking help when one is in over one’s head, etc. John DeGioia, President of Georgetown University, quoted St. Augustine in his letter of support for Georgetown Law Student, Sandra Fluke against the crude attacks by radio personality Rush Limbaugh and I think those words are quite applicable here as well.  Augustine implored his interlocutors to “lay aside arrogance” and to “let neither of us assert that he has found the truth; let us seek it as if it were unknown to both.” This is not a bad description of the way that science really should work.

What room and role do you see for normative research in your field?

I believe that there is actually an enormous amount of room for normative research, if by “normative research,” we mean research that has the potential to have a positive effect on people’s lives. If instead we mean imposing values on people, then I am less sure of its role.

Anthropology is often criticized from outside the field, and to a lesser extent, from within it for being overly politicized. You can see this in Nicholas Wade’s critical pieces in the New York Times Science Times section following the American Anthropological Association’s executive committee excising of the word “science” from the field’s long-range planning document. Wade writes,

The decision [to remove the word ‘science’ from the long-range planning document] has reopened a long-simmering tension between researchers in science-based anthropological disciplines — including archaeologists, physical anthropologists and some cultural anthropologists — and members of the profession who study race, ethnicity and gender and see themselves as advocates for native peoples or human rights.

This is a common sentiment. And it is a complete misunderstanding. It suggests that scientists can’t be advocates for native peoples or human rights.  It also suggests that one can’t study race, ethnicity, or gender from a scientific perspective.  Both these ideas are complete nonsense.  For all the leftist rhetoric, I am not impressed with the actual political practice of what I see in contemporary anthropology. There is plenty of posturing about power asymmetries and identity politics but it is always done in such a mind-numbingly opaque language and with no apparent practical tie-in to policies that make people’s lives better. And, of course, there is the outright disdain for “applied” work one sees in elite anthropology departments.

Writing specifically about Foucault, Chomsky captured my take on this whole mode of intellectual production:

The only way to understand [the mode of scholarship] is if you are a graduate student or you are attending a university and have been trained in this particular style of discourse. That’s a way of guaranteeing…that intellectuals will have power, prestige and influence. If something can be said simply, say it simply, so that the carpenter next door can understand you. Anything that is at all well understood about human affairs is pretty simple.

Ultimately, the simple truths about human affairs that I find anyone can relate to are subsistence, health, and the well-being of one’s children. These are the themes at the core of my own research and I hope that the work I do ultimately can effect some good in these areas.

Risk Management: The Fundamental Human Adaptation

It was a conceptually dense week in class.  The first part of the week I spent talking about topics such as ecological complexity, vulnerability, adaptation, and resilience. One of the key take-home messages of this material is that uncertainty is ubiquitous in complex ecological systems.  Now, while systemic uncertainty does not mean that the world is unpatterned or erratic, it does mean that people are never sure what their foraging returns will be or whether they will come down with the flu next week or whether their neighbor will support them or turn against them in a local political fight. Because uncertainty is so ubiquitous, I see it as especially important for understanding human evolution and the capacity for adaptation. In fact, I think it’s so important a topic that I’m writing a book about it.  More on that later…

First, it’s important to distinguish two related concepts.  Uncertainty  simply means that you don’t know the outcome of a process with 100% certainty.  Outcomes are probabilistic.  Risk, on the other hand, combines both the likelihood of a negative outcome and the outcome’s severity. There could be a mildly negative outcome that has a very high probability of occurring and we would probably think that it was less risky than a more severe outcome that happened with lower probability. When a forager leaves camp for a hunt, he does not know what return he will get.  10,000 kcal? 5,000 kcal? 0 kcal? This is uncertainty.  If the hunter’s children are starving and might die if he doesn’t return with food, the outcome of returning with 0 kcal worth of food is risky as well.

Human behavioral ecology has a number of elements that distinguish it as an approach to studying human ecology and decision-making.  These features have been discussed extensively by Bruce Winterhalder and Eric Smith (1992, 2000), among others.  Included among these are: (1) the logic of natural selection, (2) hypothetico-deductive framework, (3) a piecemeal approach to understanding human behavior, (4) focus on simple (strategic) models, (5) emphasis on behavioral strategies, (6) methodological individualism.  Some others that I would add include: (7) ethological (i.e., naturalistic) data collection, (8) rich ethnographic context, (9) a focus on adaptation and behavioral flexibility in contrast to typology and progressivism.  The hypothetico-deductive framework and use of simple models (along with the logic of selection) jointly accounts for the frequent use of optimality models in behavioral ecology. Not to overdo it with the laundry lists, but optimality models also all share some common features.  These include: (1) the definition of an actor, (2) a currency and an objective function (i.e., the thing that is maximized), (3) a strategy set or set of alternative actions, and (4) a set of constraints.

For concreteness’ sake, I will focus on foraging in this discussion, though the points apply to other types of problems. When behavioral ecologists attempt to understand foraging decisions, the currency they overwhelmingly favor is the rate of energy gain. There are plenty of good reasons for this.  Check out Stephens and Krebs (1986) if you are interested. The point that I want to make here is that, ultimately, it’s not the energy itself that matters for fitness.  Rather it is what you do with it. How does a successful foraging bout increase your marginal survival probability or fertility rate? This doesn’t sound like such a big issue but it has important implications. In particular, fitness (or utility) is a function of energy return.  This means that in a variable environment, it matters how we average.  Different averages can give different answers. For example, what is the average of the square root of 10 and 2? There are two ways to do this: (1) average the two values and take the square root (i.e., take the function of the mean), and (2) take the square roots and average (i.e., take the mean of the function). The first of these is \sqrt{6}=2.45. The second is (\sqrt{10} + \sqrt{2})/2=2.29.  The function of the mean is greater than the mean of the function.  This is a result of Jensen’s inequality. The square root function is concave — it has a negative second derivative. This means that while \sqrt{x} gets bigger as x gets bigger (its first derivative is positive), the increase is incrementally smaller as x gets larger. This is commonly known as diminishing marginal utility.

Lots of things naturally show diminishing marginal gains.  Imagine foraging for berries in a blueberry bush when you’re really hungry.  When you arrive at the bush (i.e., ‘the patch’), your rate of energy gain is very high. You’re gobbling berries about as fast as you can move your hands from the bush to your mouth. But after you’ve been there a while, your rate of consumption starts to slow down.  You’re depleting the bush.  It takes longer to pick the berries because you have to reach into the interior of the bush or go around the other side or get down on the ground to get the low-hanging berries.

berryplot

Chances are, there’s going to come a point where you don’t think it’s worth the effort any more.  Maybe it’s time to find another bush; maybe you’ve got other important things to do that are incompatible with berry-picking. In his classic paper, Ric Charnov derived the conditions under which a rate-maximizing berry-picker should move on, the so-called ‘marginal value theorem’ (abandon the patch when the marginal rate of energy gain equals the mean rate for the environment). There are a number of similar marginal value solutions in ecology and evolutionary biology (they all arise from maximizing some rate or another). Two other examples: Parker derived an marginal value solution for the optimal time that a male dung fly should copulate (can’t make this stuff up). van Baalen and Sabelis derived the optimal virulence for a pathogen when the conditional probability of transmission and the contact rate between infectious and susceptible hosts trade off.

So, what does all this have to do with risk? In a word, everything.

Consider a utility curve with diminishing marginal returns.  Suppose you are at the mean, indicated by \bar{x}. Now you take a gamble.  If you’re successful, you move to x_1 and its associated utility.  However, if you fail, you move down to x_0 and its associated utility.  These two outcomes are equidistant from the mean. Because the curve is concave, the gain in utility that you get moving from \bar{x} to x_1 is much smaller than the loss you incur moving from \bar{x} to x_0.  The downside risk is much bigger than the upside gain.  This is illustrated in the following figure:

risk-aversion

When returns are variable and utility/fitness is a function of returns, we can use expected utility as a tool for understanding optimal decisions. The idea goes back to von Neumann and Morgenstern, the fathers of game theory. Expected utility has received some attention in behavioral ecology, though not as much as it deserves.  Stephens and Krebs (1986) discuss it in their definitive book on foraging theory.  Bruce Winterhalder, Flora Lu, and Bram Tucker (1999) have discussed expected utility in analyzing human foraging decisions and Bruce has also written with Paul Leslie (2002; Leslie & Winterhalder 2002) on the topic with regard to fertility decisions.  Expected utility encapsulates the very sensible idea that when faced with a choice between two options that have uncertain outcomes, choose the one with the higher average payoff. The basic idea is that the world presents variable pay-offs. Each pay-off has a utility associated with it. The best decision is the one that has the highest overall expected, or average, utility associated with it. Consider a forager deciding what type of hunt to undertake. He can go for big game but there is only a 10% chance of success. When he succeeds, he gets 10,000 kcal of energy. When he fails, he can almost always find something else on the way back home to bring to camp. 90% of the time, he will bring back 1,000 kcal.  The other option is to go for small game, which is generally much more certain endeavor. 90% of the time, he will net 2,000 units of energy.  Such small game is remarkably uniform in its payoff but sometimes (10%) the forager will get lucky and receive 3,000 kcal. We calculate the expected utility by summing the products of the probabilities and the rewards, assuming for simplicity in this case that the utility is simply the energy value (if we didn’t make this assumption, we would calculate the utilities associated with the returns first before averaging).

Big Game: 0.1*10000 + 0.9*1000 = 1900

Small Game: 0.9*2000 + 0.1*3000 = 2100

Small game is preferred because it has higher expected utility.

We can do a bit of analysis on our utility curve and show something very important about risk and expected utility. I’ll spare the mathematical details, but we can expand our utility function around the mean return using a Taylor series and then calculate expectations (i.e., average) on both sides.  The resulting expression encapsulates a lot of the theory of risk management. Let w(x) indicate the utility associated with return x (where I follow the population genetics convention that fitness is given by a w).

 \overline{w(x)} = w(\bar{x}) + \frac{1}{2} w? \mathrm{Var}(x).

Mean fitness is equal to the fitness of the mean payoff plus a term that includes the variance in x and the second derivative of the utility function.  When there is diminishing marginal utility, this will be negative.  Therefore, variance will reduce mean fitness below the fitness of the mean. When there is diminishing marginal utility, variance is bad. How bad is determined both by the magnitude of the variance but also by how curved the utility curve is.  If there is no curve, utility is a straight line and w?=0.  In that case, variance doesn’t matter.

So variance is bad for fitness.  And variance can get big. One can imagine it being quite sensible to sacrifice some mean return in exchange for a reduction in variance if this reduction outweighed the premium paid from the mean. This is exactly what we do when we purchase insurance or when a farmer sells grain futures.  This is also something that animals with parental care do.  Rather than spewing out millions of gametes in the hope that it will get lucky (e.g., like a sea urchin), animals with parental care use the energy they could spend on lots more gametes and reinvest in ensuring the survival of their offspring. This is probably also why hunter-gatherer women target reliable resources that generally have a lower mean return than other available, but risky, items.

It turns out that humans have all sorts of ways of dealing with risk, some of them embodied in our very biology.  I’m going to come up short in enumerating these because this is the central argument of my book manuscript and I don’t want to give it away (yet)! I hope to blog here in the near future about three papers that I have nearly completed that deal with risk management and the evolution of social systems, reproductive decision-making in an historical population, and foraging decisions by contemporary hunter-gatherers.  When they come out, my blog will be the first to know!

References

Charnov, E. L. 1976. Optimal foraging: The marginal value theorem. Theoretical Population Biology. 9:129-136.

Leslie, P., and B. Winterhalder. 2002. Demographic consequences of unpredictability in fertility outcomes. American Journal of Human Biology. 14 (2):168-183.

Parker, G. A., and R. A. Stuart. 1976. Animal behavior as a strategy optimizer: evolution of resource assessment strategies and optimal emigration thresholds. American Naturalist. 110 (1055-1076).

Stephens, D. W., and J. R. Krebs. 1986. Foraging theory. Princeton: Princeton University Press.

van Baalen, M., and M. W. Sabelis. 1995. The dynamics of multiple infection and the evolution of virulence. American Naturalist. 146 (6):881-910.

Winterhalder, B., and P. Leslie. 2002. Risk-sensitive fertility:The variance compensation hypothesis. Evolution and Human Behavior. 23:59-82.

Winterhalder, B., F. Lu, and B. Tucker. 1999. Risk-sensitive adaptive tactics: Models and evidence from subsistence studies in biology and anthropology. Journal of Archaeological Research. 7 (4):301-348.

Winterhalder, B., and E. A. Smith. 2000. Analyzing adaptive strategies: Human behavioral ecology at twenty-five. Evolutionary Anthropology. 9 (2):51-72.

Complexity and Nihilism

This week in class I tried to take on the topic of complexity, as in “complex systems theory.”  Complexity is a very important topic in human ecology, and biosocial science more generally.  It’s also a topic that worries me a bit. It worries for two reasons. First, it seems all too easy for people to fall in with the cult of complexity and I believe that the weight of evidence shows very clearly that people are not at their best when they are associated with cults. If a perspective on science provides novel (especially testable!) insights, then I’m all for it. When it takes on the doctrinaire elements of a religion, then I’m less convinced of its value.  The second reason complexity worries me is clearly related to the first. I am continually frustrated by anthropologists who, when confronted with complexity, throw their hands up and say it’s too complex to make predictions, why bother to do science or understand the principles underlying the system?  You’d need to be trained as a theoretical physicist to understand the theory and people who think they understand something are just deluding themselves (or at least the rest of us) with a masculinist, hegemonic fantasy anyway. Let’s just tell a narrative (preferably peppered with some mind-numbing post-structuralist social theory). Better, perhaps, that we describe history. I think that this view is misguided to say the least (though I agree that history is fundamentally important).

There are three very influential reviews, all written for the Annual Review of Anthropology (when Bill Durham was editor, might I add), by eminent ecological anthropologists that have fed this perspective. Ian Scoones, Steve Lansing, and William Baleé each wrote a review between 1999 and 2006 more or less on the topic of complexity in human ecology. Scoones (1999) reviewed the ‘New Ecology’ and its implications for the social science. Lansing (2003) introduced complexity proper , and Baleé (2006) wrote about ‘Historical Ecology.’ I think its probably fair to say that each of these authors has a different sensibility regarding the role of science in anthropology.

Baleé advocates for the perspective of historical ecology, which emphasizes historical contingency and human agency in shaping landscapes.  He seems to conflate systems ecology with an equilibrium episteme, noting that historical ecology is ‘at odds with systems ecology’ (Baleé 2006: 81) for the latter approach’s inability to allow human agency to increase biodiversity in some cases.  This is an odd critique, since there is nothing inherent in any systems theory of ecological dynamics that makes this the case.  He is also critical of island biogeography theory of MacArthur & Wilson (1967) because of its lack of attention to human agency as a cause of species invasions. Again, there is nothing inherent in island biogeography theory — or its modern inheritor, metapopulation biology — that excludes human agency as a mechanism for colonization. Presumably, the interested anthropologist could construct a model that included human facilitation of species invasions and explore both the transient and asymptotic (e.g., equilibrium) properties of this model.

Systems ecology, according to Baleé’s review, may have provided mathematical rigor to human ecology but it was static, ahistorical, and neglected political processes, a point first noted by Wolf in his Europe and the People without History. While it is certainly true that cultural ecologists studied relatively unstratified cultures (typically in isolation of other parts of the (human) world economic system), once again, there is nothing intrinsic in cultural ecology that makes this necessary. The idea of a cultural core (“the constellation of features which are most closely related to subsistence activities and economic arrangements” (Steward 1955:37)), central to Steward’s cultural ecology, is entirely applicable to stratified societies. It is more complex but that doesn’t make it irrelevant. Similarly, it seems that Steward’s multilinear evolutionary theory of culture, with its focus on broad cross-cultural patterns but emphasis of local particularities is also largely compatible with the tenets of historical ecology. I think that it is a fundamental misapprehension that every anthropologist who studies subsistence of face-to-face groups, following in the tradition of Julian Steward, is unaware of the larger political entanglements of foraging, farming, or pastoral people in a larger world political-economic system (see, e.g., Doug Bird‘s nice essay on the politics of Martu foraging). There is just a conditionality — or ‘bracketing’ if you prefer the phenomenological term — of subsistence activities.  Given that the Martu or Hadza (or whoever) forage, how do they go about doing it? What are the consequences for landscapes in which they are embedded? These are legitimate, important, and interesting questions.  So are questions about broader political economy.  A little secret: They’re not mutually exclusive.

Lansing writes about complex systems proper, and about the phenomenon of emergence in particular.  Emergence occurs when order arises solely out of local interactions and in the absence of central control. I agree completely with Lansing that an investigation of emergence is an important endeavor in ecological anthropology and, indeed, anthropology more generally. My concern that emerges from Lansing’s paper is simply the idea that we have no hope of understanding anything without really complex nonlinear models — models that are so complex they can only be instantiated in agent-based simulations. While I am engaged in the ideas of complex systems, I am not quite ready to give up on many traditional forms of analysis that use linear models. As we will see below, the devil is in the details in complex systems models and I don’t think it’s good for science to deprive ourselves of important suites of tools because of a priori assumptions about the nature of the systems we study. This statement should not be interpreted to mean that I think this is what Lansing is doing. I do worry about anthropologists who read this review being scared away from formal ecological analysis because the nonlinearity sounds scary.

It is Scoones (1999) who makes the most extreme statements about the consequences of complexity for human ecology.  Regarding the three unifying themes around which the new human ecology was coalescing, he writes (1999: 490), “Third is the appreciation of complexity and uncertainty in social-ecological systems and, with this, the recognition of that prediction, management, and control are unlikely, if not impossible.” I think that this statement, while it may be an accurate description of some unifying themes in recent human ecology is simply incorrect and more than a bit nihilist. In all fairness, Scoones goes on to ask what the alternatives to the usual practice are (1999: 495):

So, what is the alternative to such a managerialist approach? A number of suggestions have been made. They generally converge around what has been termed “adaptive management” (Holling 1978, Walters 1976). This approach entails incremental responses to environmental issues, with close monitoring and iterative learning built into the process, such that thresholds and surprises can be responded to (Folke et al 1998).

This is a fair statement, which is rather at odds with the previous quote. If prediction and management are impossible, why is adaptive management a viable replacement?  Does adaptive management not entail making predictions and, well, managing? Of course it does.

I have a series of critical questions that must be addressed before we accede to excessive complexity and stop trying to understand the process underlying human ecology.

  1. With nonlinearity (as with stochasticity), the devil is in the details. What is the shape of the response? Sometimes nonlinear models are remarkably linear over the relevant parameter space and time scope.  Sometimes they’re not.  We don’t know unless we ask.
  2. What is the strength of the response? With nonlinearity, the thing that matters for the difficulty in prediction, sensitivity to initial conditions, etc. is the strength of response. Sometimes this strength is not that high and linear models work amazingly well.
  3. How big are the possible perturbations? We might be able to make quite good predictions if perturbations are small. Of course, we shouldn’t assume that perturbations are always small (as much classical analysis does).  This is an empirical question.
  4. What is the effect of random noise?  Some of the deterministic models with exotic dynamics collapse into pretty standard models in the presence of noise.  Of course, sometimes randomness makes prediction even harder — this is partly a function of the previous three points (i.e., the shape of nonlinearity, the strength of the response, and the size of perturbations).

A couple figures can illustrate two of these points.  Consider the following hypothetical recruitment plot.  On the x-axis, I have plotted the population size, while on the  y-axis, I have plotted the number of recruits born. Suppose that the actual underlying process for recruitment was density-dependent (i.e., was nonlinear), as indicated by the dashed line. In this particular hypothetical case, you would not do all that badly with a linear model (solid line).  As we move across three orders of magnitude, the difference in recruitment between the linear and nonlinear models is two births. The process of recruitment is nonlinear (i.e., it’s density-dependent) but you would do just fine with predictions based on a linear model.

linear-nonlin-comp

Taking up on Bob May’s classic (1976) paper, we can use the logistic map (a discrete-time logistic population growth model)  to look at strength of response.  The logistic map is given by the following nonlinear difference equation X_{t+1} = a X_t (1 ? X_t). We can plot the relationship between X_t and X_{t+1}.  This shows the classic symmetric, humped recruitment curve characteristic of the logistic model.  Where a line X_{t+1} = X_t intersects the recruitment curve, the model has a fixed point. The stability of these fixed points is determined by the slope of the tangent line at the intersection of the curves. If the absolute value of this slope is greater than one, perturbations from the fixed point will grow — the model is unstable.  If the absolute value of this slope is less than one, then any trajectory in the neighborhood will return to the fixed point. The parameters used to make these figures create a complex 2-point series (i.e., the population oscillates between two fixed points) on the left-hand case, while for the right-hand case, there is a simple fixed point. By cranking up the parameter a in the logistic map, we can induce more and more exotic dynamics.  However, the key point here is that if the response is weak enough, the dynamics are not especially exotic at all. Note that we start to get the interesting behavior at values of a>3, or a tripling of population size each time step.  Human populations do not grow nearly this fast.  Not even close. This isn’t to say that some human processes with nonlinear dynamics don’t have very strong responses, but clearly not all must. Population growth is a pretty important problem for human ecology, and it’s dynamics are unlikely to be really exotic.  Maybe we can use some simple models to understand human population dynamics?  See last week’s post on the work of Tuljapurkar and colleagues for some exemplary contemporary work.

response-strength

So, there are two cases where understanding the nature of the nonlinearity makes an enormous difference in how we make predictions and otherwise understand the system.  Sometimes nonlinear models are effectively linear over important ranges of parameter space.  Sometimes the response of a nonlinear model is small enough that the system shows very predictable, well-mannered dynamics. But just so you don’t think that I don’t think complexity is an issue, let’s look at one more example.  This model is from a classic study by Hastings and Powell (1991) showing chaos in a simple model of a food chain.

The model has three species: producer, primary consumer, secondary consumer; and it is a simple chain (secondary consumer eats primary consumer eats producer). Hastings and Powell chose the model parameters to be biologically realistic — there’s nothing inherently wacky about the way the model is set up. Using the same parameters that they use to produce their figure 2, I numerically solved their equations (using deSolve in R).  The first plot shows the dynamics in time, with the bizarre oscillations in all three species.

series

In the second figure, I reproduce (more or less) their three-dimensional phase plot, which takes time out of the plot and instead plots the three population series directly against each other.

3d-phase

Finally, I plot some pair-wise phase-plots, which are easier to visualize than the false 3D image above.

phase-planes

On the whole, we see very complex behavior in a rather simple food chain. Hastings and Powell (1991: 901-902) summarize their findings: (1) contrary to conventional wisdom, they suggest that chaos need not be rare in nature, (2) chaotic behavior “need not lead to an erratic and unpatterned trajectory in time that one might infer from the usual (not mathematical) connotation of the word ‘chaos'” and (3) time scales matter tremendously — over short time scales, the behavior of the system is quite regular.

For me, the greatest lesson from the complex systems approach is the need to understand the specific details.  Contrary to the inclination to throw up one’s hands at the thought of a science of human ecology (let alone putting this science into practice with sensible management policies), it seems that the issues raised here mean that we should study these systems more, attempting to understand both their historical trajectories and the principles upon which they are organized. By all means, let’s jettison old-fashioned ideas about typology and homeostasis in nature.  No need to keep around the clockworks metaphor of ecological succession or the idea that the Dobe !Kung are Pleistocene remnants. Ecosystems, landscapes, whatever term you want to use, don’t necessarily tend toward equilibria. Uncertainty is ubiquitous. People are part of these systems and have been for a long time. Good, we’re agreed.  But can we please not give up on using all the scientific tools we have at our disposal to understand these complex systems in which human beings are embedded? Anthropologists have much to contribute to this area, not the least of which is long-term, place-based research on human-environmental systems.

The lesson of prediction over the short-term is another issue that comes up repeatedly in the complex systems literature.  I think that the work of George Sugihara and colleagues is especially good on this front. I have blogged (here and here) about a paper on which he is a co-author before (I should note that in this paper they suggest ways to make predictions of catastrophic events in complex systems with noise — just sayin’). There is a nice, readable article in Scientific American on his work on fisheries that summarizes the issues. This work combines so many things that I like (demography, fish, statistics, theoretical ecology, California), it’s a bit scary. Another nice, readable piece that also describes some of Sugihara’s work in finance can be found in SEED magazine here.

This post is already too long.  I clearly will need to write about the other topic for the week, risk and uncertainty, at a later date.

References

Baleé, W. 2006. The research program of historical ecology. Annual Review of Anthropology. 35:75-98.

Hastings, A., and T. Powell. 1991. Chaos in a three-species food chain. Ecology. 72 (3):896-903.

Lansing, J. S. 2003. Complex adaptive systems. Annual Review of Anthropology. 32:183-204.

MacArthur, R. H., and E. O. Wilson. 1967. The theory of island biogeography. Princeton: Princeton University Press.

May, R. M. 1976. Simple Mathematical-Models with Very Complicated Dynamics. Nature. 261 (5560):459-467.

Scoones, I. 1999. New Ecology and the social sciences: What prospects for a fruitful engagemnt? Annual Review of Anthropology. 28:479-507.

Response to Selection

I’m done now with the first week of the Spring quarter. It was a bit challenging because I had to attend the PAA meetings in Washington, DC for the latter part of the week, but Brian Wood ably covered for me on Thursday. I thought that I would use the blog as a tool for summarizing one of the key points I want students to take away from this fist week in which we discussed evolution and natural selection.

We spent a good deal of lecture time talking about adaptation.  Specifically, we discussed how adaptation can serve as a foil to typology and essentialism. Adaptation is local and must be seen within its specific environmental and historical context. Adaptations are dynamic because environments are.

Adaptationist thinking is powerful, but can easily be overdone. This is why I also think it is essential to understand the mechanics of selection, something that I’m afraid is not often addressed in introductory evolutionary anthropology classes.  So, in the very first lecture of class, I throw some quantitative genetics (and, thus, some math) at students.  Of course, these are Stanford students, so I’m confident they can handle a little techie-ness every now and then. We specifically discuss the multivariate breeder’s equation, sometimes known as Lande‘s equation:

\Delta \mathbf{\bar{z}} = \mathbf{G \beta}

,

where \Delta \mathbf{\bar{z}} is the change in the mean fitness of a multivariate trait, \mathbf{G} is the additive genetic variance-covariance matrix, and \beta is the selection gradient on \mathbf{\bar{z}}.

In effect, \beta is a vector pointing in the direction of the optimal change in the phenotype. The matrix \mathbf{G} does two things to this gradient pushing \mathbf{\bar{z}} toward its optimum: (1) it scales the response depending on how much additive variance there is in each trait and (2) it rotates it as a function of the covariances between traits. I won’t get too much into matrix multiplication here (this is a very nice reference too). The key point is that \mathbf{G} is a square k \times k matrix (where k is the number of traits we’re looking at) the diagonal elements of which are variances and the off-diagonal elements of which, g_{ij} represent the covariances between traits i and j.   Selection requires variance. Without sufficient variance, even strong selection won’t change the phenotype much between generations.  But variance isn’t all there is to it. When the covariances are positive, there will be substantial indirect selection, and when they are negative, you have genetic constraints at work. Selection may be pointing in a particular direction, but the structure of the trade-offs could very easily mean that you can’t actually get there.

Let’s consider three quick (toy) examples.  Say we have two traits, maybe “length” and “width” (this could be something less vague and insipid: Lande (1979) looks at brain mass and body mass in a serious two-trait example). We will assume that the selection gradient is \mathbf{\beta} = \{0.5, 0.25\}?. That is, the force of selection is twice as high on length as it is on width, but it is pretty strong and positive on both. We’ll demonstrate the effect of variance and constraint in three ways:  (1) more variance in the trait under weaker selection (\mathbf{G_1}), (2) positive covariance between the two traits (\mathbf{G_2}), and (3) negative covariance between the two traits (\mathbf{G_3}).

 \mathbf{G_1} = \left( \begin{array}{cc} 0.33 & 0.00 \\ 0.00 & 0.67 \end{array} \right)

 \mathbf{G_2} = \left( \begin{array}{cc} 0.33 & 0.33 \\ 0.33 & 0.67 \end{array} \right)

 \mathbf{G_3} = \left( \begin{array}{cc} 0.33 & -0.33 \\ -0.33 & 0.67 \end{array} \right)

The figure below plots the response to selection in the three different types of genetic architecture.  The direction of selection is indicated in the grey arrow. If the variances of the two traits were equal to 1 and there were zero covariances, this is where selection would move the phenotype pair (try it). We can see that the response to selection moves toward width (the trait under weaker selection) even when covariances are zero (black arrow).  Why? Because there is more variance for width than there is for length (0.67 \times 0.25 > 0.33 \times 0.5).  This effect becomes more pronounced when there is positive covariance between the traits (blue arrow) — the selection toward width is 0.33 \times 0.5 +0.67 \times 0.25 = 0.3325. When the covariances are negative, we see something cool (red arrow).  The response to selection is small and moves (almost) entirely in the direction of length. This is because the negative covariance between length and width, when acted on by the strong selection on length, all but cancels out the positive response to selection (-0.33 \times 0.5 + 0.67 \times 0.25 = 0.0025).

selection-constraint-plot

This simple demonstration shows that the response to selection can be complex. Making an argument that some trait would be under selection is not sufficient to say that it actually evolved (or will evolve) that way.  Entirely plausible arguments for the direction of selection are made all the time in evolutionary anthropology.  Here is one from a very important paper in paleoanthropology (Lovejoy 1981: 344):

Any behavioral change that increases reproductive rate, survivorship, or both, is under selection of maximum intensity. Higher primates rely on social behavioral mechanisms to promote survivorship during all phases of the life cycle, and one could cite numerous methods by which it theoretically could be increased.  Avoidance of dietary toxins, use of more reliable food sources, and increased competence in arboreal locomotion are obvious examples. Yet these are among the many that have remained under stadong selection throughout much of the course of primate evolution, and therefore unlikely that early hominid adaptation was a product of intensified selection for adaptations almost universal to anthropoid primates.

Arguing for selection without considering trade-offs can get you into trouble.  Selection in the presence of quantitative genetic constraints (or even differential variance in the traits) can produce counter-intuitive results. (Selectionists, don’t dispair. There are ways to deal with this, but it will have to wait for another post). In the case of Lovejoy’s argument, there are good reasons to think that survivorship and reproductive rate are, indeed, strongly negatively correlated. Which is under stronger selection? Which has more additive variance? How strong are the negative covariances?

When we make selectionist or adaptationist arguments, we should always keep in the back of our minds the three questions:

  1. How strong is the force of selection?
  2. How much variance is there on which selection can act?
  3. How is the trait constrained through negative correlations with other traits?

References

Lande, R. A. 1979. Quantitative genetic analysis of multivariate evolution applied to brain: body size evolution. Evolution. 33:402-416.

Lovejoy, C. O. 1981. The origin of man. Science. 211:341-350.

Ecology, Evolution, and Human Health

Yesterday, I spent most of the day collecting content for my upcoming classes this spring and getting the course web sites together.  For the first time in a while, I will (officially) be teaching two classes in one quarter (which effectively means teaching three or four when I add the other things like lab meetings in).  The first is our graduate class on statistics in the anthropological sciences.  I taught something like this back in the old department (i.e., Anthropological Sciences) but haven’t taught it in years (though a Google search for “department of anthropological sciences stanford” turns up the syllabus for this class).  It is technically a requirement for Ph.D. students in the Ecology and Environment focus within Anthropology, so it’s about time.  It will be fun to teach again and we’re looking to use the class as a platform to develop resources for anthropologists doing statistical work (more later).

The other class that I will be teaching starting next week is Ecology, Evolution, and Human Health, a class I first taught last year. This class is meant to be an introduction to the Ecology and Environment undergraduate focus in Anthropology.  I’m actually really looking forward to teaching it again.  The course material forms the core of a book I am writing on human population biology and my attempts at improving the lectures has done wonders for my writing output of late.  We’ll see what happens when the quarter actually starts. Hopefully, between trips to Rwanda and Tanzania and moving into Arroyo House this summer, I will find time to finish it!

Back in December, when the is-anthropology-science kerfuffle was going strong, I wrote a blog post in which I suggested that if you want to feel good about the future of scientific anthropology (which, I admit, can sometimes be difficult, even for an obstinate optimist), all you need to do is look at the great work coming from the new generation of trans-disciplinary anthropologists (and other biosocial scientists).  At the time, I put together a short list of people whose work I greatly admire.  These included:

  • Craig Hadley at Emory on food security and psychological well-being
  • Amber Wutich at ASU on vulnerability, water security, and common-pool resources
  • Lance Gravlee at UF on the embodiment of racial discrimination and its manifestations in health
  • Brooke Scelza at UCLA on parental investment and childhood outcomes
  • Dan Hrushka at ASU on how cultural beliefs, norms and values interact with economic constraints to produce health outcomes
  • Crickette Sanz at Washington University on multi-ape ecology of the Goualougo Triangle, Republic of Congo
  • Herman Pontzer at CUNY on measuring daily energy expenditures in hunter-gatherers
  • Rebecca and Douglas Bird on subsistence and signaling among Martu foragers

In preparing for Anthro 31, I started to put together a list of links to people doing the kind of work we will discuss.  In a pique of obsessiveness yesterday, I greatly expanded that list.  It occurred to me that this list is somewhat orphaned in an obscure directory for a particular class I occasionally teach and that it would make sense to share it more generally.  So, here we go, copied wholesale from my class links page (though that page still contains links to books, professional societies, and other resources for students interested in human ecology, demography, health, etc.):

There are a number of excellent practicing anthropologists who maintain science blogs. Among these are Kate Clancy‘s (UIUC) Context and Variation, Daniel Lende and Greg Downey‘s Neuroanthropology, Julienne Rutherford‘s AAPA BANDIT, and Patrick Clarkin’s blog dedicated to biological anthropology, war and health, growth nutrition. Along with Rebecca Stumpf, Kate Clancy is also the director of the Laboratory for Evolutionary Endocrinology (which has its own blog) at the University of Illinois.

Upon further reflection, I think that the University of Illinois has to be a major contender for best place to study biological anthropology. Wow, they’ve got an amazing group of biological anthropologists there. Stanley Ambrose, Kate Clancy, Paul Garber, Lyle Konigsberg, Steve Leigh, Ripan Malhi, John Polk, Charles Roseman, Laura Shackelford, Rebecca Stumpf. Too many to link to directly. I don’t know all of them, but the ones I know are outstanding. Yipes! I think they may be plotting to take over the field.

Back to the blog front, you can always count on gems of anthropological, evolutionary, and political wisdom from Greg Laden as well.

Susan C. Antón (NYU) and Josh Snodgrass (Oregon) organize the Bones and Behavior Working Group, the goal of which is to foster greater synthesis across the different sub-areas of biological anthropology. Of particular interest are their standardized protocols for anthropometry.

Mario Luis Small, at the University of Chicago, has done some really outstanding work measuring how social institutions affect social capital and the impact such differences in social capital actually have for people’s well-being.

Richard Bribiescas is the author of Men: Evolutionary and Life History and is director of the Reproductive Ecology Laboratory at Yale. Yale is also now the home to Catherine Panter-Brick who also happens to be the senior editor for medical anthropology at Social Science and Medicine.

A number of excellent human biologists find their home in the Laboratory for Human Biology Research at Northwestern. This includes Bill Leonard, Thom McDade, and Chris Kuzawa. Rumor has it that alumna Elizabeth Sweet is moving back to Northwestern as well. She is doing truly innovative work integrating the rigorous analysis of biomarkers of health (and a bicultural perspective favored by the Northwestern group) and the political economy of economic and social disparities — really getting at how inequality ‘gets under the skin.’  I really look forward to seeing what comes from her future research.

Karen Kramer, in the department formerly known as (Biological) Anthropology at Harvard, is a real leader in integrating evolutionary, demographic, and economic perspectives on human reproduction and the life histories.

Patrick Clarkin at UMass, Boston has a very interesting research program employing biocultural and evolutionary models to understand the effects of war on nutrition and growth among SE Asian diaspora. UMass, Boston is also home to Colleen Nyberg who does great work on acculturation and health, the psychobiology of stress and HPA function, and growth and development.

Julienne Rutherford at the University of Illinois, Chicago School of Dentistry works on the role of the intrauterine environment on health. Of particular interest for this class is her collaborative work on understanding the epigenetic regulation of placental systems of amino acid transport as part of the Cebu Longitudinal Study in the Philippines. UIC also has a number of excellent human biologists scattered about in anthropology, including Betsy Abrams and Crystal Patil, Epidemiology (Bob Bailey) and Community Health Sciences (Nadine Peacock).

Let’s not forget our friends across The Pond. Durham may have lost Catherine Panter-Brick to Yale, but they got a number of new folks who, when combined with the veterans, make it a very appealing place to study ecological/evolutionary anthropology. Among the faculty there are my colleagues Gillian Bentley, Rebecca Sear, and Frank Marlowe, and numerous others. Rebecca does very sophisticated work in anthropological demography, while Frank is one of the leading ethnographers of contemporary hunter-gatherers (and my collaborator on our Hadza demography project).

Ruth Mace, in my opinion, does some of the best work in human behavioral ecology right now and she keeps churning out top students at UCL.

I’m looking forward to working with Mhairi Gibson at Bristol on our new project on the transmission dynamics of primate retroviruses and human-wildlife contact in Uganda. She has done excellent work on the behavioral ecology of reproduction and parental investment in Ethiopia.

I will also mention a number of excellent researchers who teach classes that are relevant to Ecology, Evolution, and Human Health:

Mark Moritz at Ohio State University has established a Hunter-Gatherer Wiki is conjunction with his course on Hunter-Gatherers. Mark came and gave a terrific talk on livestock exchanges among FulBe pastoralists at the MAPSS colloquium this year.

Mike Gurven at UCSB teaches a course on the behavioral ecology of hunter-gatherers. Mike does some of the most interesting biodemographic work out there these days.

Bruce Winterhalder at UC Davis, a founding father of human behavioral ecology, has a very interesting course on classics in cultural ecology.

Claudia Valeggia, at Penn, does great work among the Toba people of Argentina teaches a class on reproductive ecology.

Lots of good people. Lots of good work.  Surely, there is reason for optimism…

New Formal Demography Workshop: Migration and Adaptation

We will be having another of our occasional Stanford Workshops in Formal Demography this April 28th-30th. The theme this time will be “Migration and Adaptation,” and we have a terrific lineup of speakers coming. As in the past, the workshop is funded by NICHD and receives substantial suport from the Stanford Institute for Research in the Social Sciences (IRiSS). What is somewhat different this time is that we actually have our own center now, The Stanford Center for Population Research (SCPR). Here’s the basic idea for the workshop:

Mobility is a common form of human adaptation to social or environmental risks.  Forms of human mobility vary with regard to permanency and spatial scale.  For example, foragers or pastoralists may move seasonally in response to resource scarcity and opportunity throughout a more or less stable greater home range. Smallholders and agrarian peasants might be displaced on a more permanent basis as a result of conflict or extreme resource scarcity, migrating internally to cities or other relatively nearby localities perceived to be less risky.  International economic migrants may travel long distances on a more or less permanent basis in search of economic opportunity abroad.

Global climate change is predicted to increase migration rates substantially by the middle of the 21st century.  This increase in migration is likely to result from multiple, interacting causal mechanisms including an increase in adverse weather events (e.g., droughts, floods), an increase in resource-related conflicts, or declining viability of local environments arising from various forms of land-use/land-cover change.  These increases will add to the already substantial movement of human population from rural to urban areas, in response to internal social displacement, and from other economic migration.

Understanding human migration requires the input from scientists from a wide range of disciplines. We are particularly interested in approaches that combine the formalism of demography, on-the-ground social research, and remotely-sensed information of the biophysical environment, the so-called “pixels to people” approach.

In this workshop, we will bring together demographers, anthropologists, economists, and geographers to develop a methodological toolkit for understanding migration as an adaptation to risk.  The specific aim of the workshop is to promote knowledge of methods and perspectives from different disciplines, disseminate information about the growing wealth of demographic data on the biophysical environment and human migration, and to foster collaborative and interdisciplinary work. The format will consist of lectures by invited researchers to an audience of other researchers, selected graduate students, and junior faculty. The three-day workshop will have approximately ten faculty and 20 students, whose travel, lodging, and meals will be covered.  The format provides substantial time for discussion. The workshop will be held at the Institute for Research in the Social Sciences (IRiSS), Stanford 28-30 April 2011.

Confirmed speakers include:

  • James Holland Jones, Department of Anthropology and Woods Institute for the Environment, Stanford University (organizer): Formal Models;
    Population Projection
  • Shripad Tuljapurkar, Department of Biology, Stanford University (organizer): Stochastic Forecasting
  • Eric Lambin, Environmental and Earth Systems Science and Woods Institute for the Environment, Stanford University: Pixels to People
  • David Lobell, Environmental and Earth Systems Science and Woods Institute for the Environment, Stanford University: Global Climate Change and Food Insecurity
  • William H. Durham, Department of Anthropology and Woods Institute for the Environment, Stanford University: Smallholder Responses to Risk and Uncertainty
  • Ronald Rindfuss, Carolina Population Center, University of North Carolina and The East-West Center: Population and Environment; Microsimulation
  • Amber Wutich, School of Human Evolution and Social Change, Arizona State University, Water Insecurity
  • Lori Hunter, Department of Sociology, University of Colorado: Migration and Health
  • David Lopez-Carr, Department of Geography, University of California Santa Barbara: Migration and Fertility on the Forest Frontier

A (rather large) printable flier for the workshop can be found here.  It includes information on how to apply.  Hopefully, we will soon have an all official-like webpage through IRiSS as well, which I will point to when it goes live.

Anthropology: A Bittersweet Love Story

Rex from Savage Minds laid out a St. Valentine’s Day challenge. He asked for love letters to anthropology, in part, as a follow-up to the #aaafail fracas of December last. He notes “there is a strong chance that I’m opening the flood gates for endless cynical, bodice-ripping parodies.” But I’ll play it straight. It just so happens that the topic plays into many of the ongoing conversations I am having with friends and colleagues these days.  So, here it goes in all earnestness…
For me, anthropology is the science charged with explaining the origin and maintenance of human diversity in all its forms. To acheive this end, anthropology must be unapologetically grand in its scope.  How can we explain human diversity without documenting its full extent,  through both time and space, and across cultures? This is the thing that drew me to anthropology, the thing that really made me fall in love with it. The great story of humanity. Our great story.  Where did we come from?  What makes us human? Where does the tapestry of human diversity come from and how is it that we continually manage to resist powerful homogenizing forces and hang on to our diversity? What commonalities transcend local difference to unite all humanity? How is it that civilizations rise and fall?  And what is the fate of humanity?
This vision of anthropology relies on a simultaneous focus on difference and universality — reminiscent of Scott Fitzgerald’s famous take on true intelligence, “the ability to hold two opposed ideas in the mind at the same time, and still retain the ability to function.” It isn’t about making hyperbolic claims on flimsy or otherwise highly situated evidence. It is about relentlessly examining the commonplace with an eye to universal, the grand.
As a practitioner who came of age after the worst of the anthropology culture wars was over, what breaks my heart about the current state of our discipline is its smallness.  Anthropology has become substantially less ambitious yet so many practitioners seem utterly satisfied with this state of affairs, in large measure because we fail to engage with other disciplines. We ask trivial questions about absurdly particularistic topics.  We hesitate to make even the most unproblematic generalizations or, worse (?), make absurd generalizations on the most meagre of evidence.  We complexify rather than analyze. We theorize rather than understand. We demonize and pigeonhole our colleagues. We prefer the clever to the correct, a trait that our know-nothing discipline ironically seems to share with our hyper-rationalist colleagues in economics.
I worry for my beloved discipline’s future.  If we continue failing to connect with humanity’s big questions — if we fail to engage a broader community — we are relegated to doing poorly-funded and theoretically unsophisticated biology, literary criticism without any texts, and telling stories that no one outside our immediate circles either believes or even cares about.
For anthropology to thrive, we need to not be afraid to learn the tools that help us answer questions we want answered, rather than simply the ones that are expedient. Better still, we should have the confidence to create our own methods and develop our own theories, rather than perpetually borrowing them from our ostensibly better-endowed cognate disciplines.
One of my great intellectual heroes is Gene Hammel. Gene is an anthropologist who has published in all four subfields of anthropology; an anthropologist who gave talks to statistics departments; an anthropologist who developed new computational tools to analyze kinship and social structure long before any social scientist had a computer on his or her desk. Gene is also an anthropologist who left his anthropology department after 40 years to join a demography department because he could no longer stand the nonense of anthropology.
I wonder if this isn’t also my fate.  Was my infatuation with the immensity of anthropology simply a passionate affair of youth?  Does the mature me move on to a more sane, more stable disciplinary home? It’s a question to which I’ve given no small amount of thought recently…
…But, as I’ve said before, and I imagine I will say again, I really believe that anthropology can play a role in meeting the enormous challenges our species now faces.  Diversity is the foundation of adaptation and adpatation is always local. Understanding how different people in different places and different times solve(d) real problems provides the raw material for finding adaptive solutions to a rapidly changing world. Despite all the rhetoric one hears about living in a global world, the need for multiculturalism, blah, blah, blah, ethnocentrism and imperialist conceit are so pervasive in the contemporary academy that I seriously doubt any other discipline is likely to pick up this particular challenge. So it’s up to anthropology. However, to make this vital contribution, anthropology needs to care about the larger picture of humanity and the planet in which we are enmeshed, and anthropologists need to have the confidence to make their marks. Maintaining love after the first blush of passion has passed takes effort. Whether my discipline/lover and I are up for the joint challenge is an open question, but regardless of the outcome of couples therapy, our early relationships shape who we are and who we can become. At the very least, I will always have this vision of a grand anthropology to help guide whatever I become.

Rex from Savage Minds laid out a St. Valentine’s Day challenge. He asked for love letters to anthropology, in part, as a follow-up to the #aaafail fracas of December last. He notes “there is a strong chance that I’m opening the flood gates for endless cynical, bodice-ripping parodies.” But I’ll play it straight. It just so happens that the topic plays into many of the ongoing conversations I am having with friends and colleagues these days.  So, here it goes in all earnestness…

For me, anthropology is the science charged with explaining the origin and maintenance of human diversity in all its forms. To achieve this end, anthropology must be unapologetically grand in its scope.  How can we explain human diversity without documenting its full extent,  through both time and space, and across cultures? This is the thing that drew me to anthropology, the thing that really made me fall in love with it. The great story of humanity. Our great story.  Where did we come from?  What makes us human? Where does the tapestry of human diversity come from and how is it that we continually manage to resist powerful homogenizing forces and hang on to our diversity? What commonalities transcend local difference to unite all humanity? How is it that civilizations rise and fall?  And what is the fate of humanity?

This vision of anthropology relies on a simultaneous focus on difference and universality — reminiscent of Scott Fitzgerald’s famous take on true intelligence, “the ability to hold two opposed ideas in the mind at the same time, and still retain the ability to function.” It isn’t about making hyperbolic claims on flimsy or otherwise highly situated evidence. It is about relentlessly examining the commonplace with an eye to universal, the grand.

As a practitioner who came of age after the worst of the anthropology culture wars was over, what breaks my heart about the current state of our discipline is its smallness.  Anthropology has become substantially less ambitious yet so many practitioners seem utterly satisfied with this state of affairs, in large measure because we fail to engage with other disciplines. We ask trivial questions about absurdly particularistic topics.  We hesitate to make even the most unproblematic generalizations or, worse (?), make preposterous generalizations on the most meagre of evidence.  We complexify rather than analyze. We theorize rather than understand. We demonize and pigeonhole our colleagues. We prefer the clever to the correct, a trait that our know-nothing discipline ironically seems to share with our hyper-rationalist colleagues in economics.

I worry for my beloved discipline’s future.  If we continue failing to connect with humanity’s big questions — if we fail to engage a broader community — we are relegated to doing poorly-funded and theoretically unsophisticated biology, literary criticism without any texts, and telling stories that no one outside our immediate circles either believes or even cares about.

For anthropology to thrive, we need to not be afraid to learn the tools that help us answer questions we want answered, rather than simply the ones that are expedient. Better still, we should have the confidence to create our own methods and develop our own theories, rather than perpetually borrowing them from our ostensibly better-endowed cognate disciplines.

One of my great intellectual heroes is Gene Hammel. Gene is an anthropologist who has published in all four subfields of anthropology; an anthropologist who gave talks to statistics departments; an anthropologist who developed new computational tools to analyze kinship and social structure long before any social scientist had a computer on his or her desk. Gene is also an anthropologist who left his anthropology department after 40 years to join a demography department because he could no longer stand the nonsense of anthropology.

I wonder if this isn’t also my fate.  Was my infatuation with the immensity of anthropology simply a passionate affair of youth?  Does the mature me move on to a more sane, more stable disciplinary home? It’s a question to which I’ve given no small amount of thought recently…

…but, as I’ve said before, and I imagine I will say again, I really believe that anthropology can play a role in meeting the enormous challenges our species now faces.  Diversity is the foundation of adaptation and adaptation is always local. Understanding how different people in different places and different times solve(d) real problems provides the raw material for finding adaptive solutions to a rapidly changing world. Despite all the rhetoric one hears about living in a global world, the need for multiculturalism, blah, blah, blah, ethnocentrism and imperialist conceit are so pervasive in the contemporary academy that I seriously doubt any other discipline is likely to pick up this particular challenge. So it’s up to anthropology. However, to make this vital contribution, anthropology needs to care about the larger picture of humanity and the planet in which we are enmeshed, and anthropologists need to have the confidence to make their marks. Maintaining love after the first blush of passion has passed takes effort. Whether my discipline/lover and I are up for the joint challenge is an open question, but regardless of the outcome of couples therapy, our early relationships shape who we are and who we can become. At the very least, I will always have this vision of a grand anthropology to help guide whatever I become.